标题： 通过细粒度补丁-文本对齐增强零样本脑肿瘤亚型分类 显示英文标题

标题： Enhancing Zero-Shot Brain Tumor Subtype Classification via Fine-Grained Patch-Text Alignment

摘要： 从组织病理学全切片图像中进行脑肿瘤亚型的细粒度分类由于细微的形态变化和标注数据的稀缺性而极具挑战性。 尽管视觉-语言模型已实现了有前景的零样本分类，但它们在捕捉细粒度病理特征方面的能力仍然有限，导致亚型区分效果不佳。 为了解决这些挑战，我们提出了细粒度补丁对齐网络（FG-PAN），这是一种专为数字病理学设计的新颖零样本框架。 FG-PAN包含两个关键模块： (1) 一个局部特征精炼模块，通过建模代表性补丁之间的空间关系来增强补丁级视觉特征，以及 (2) 一个细粒度文本描述生成模块，利用大语言模型生成病理感知的、类别特定的语义原型。 通过将精炼后的视觉特征与LLM生成的细粒度描述对齐，FG-PAN在视觉和语义空间中有效提高了类别可分性。 在包括EBRAINS和TCGA在内的多个公开病理数据集上的大量实验表明，FG-PAN在零样本脑肿瘤亚型分类中达到了最先进的性能和强大的泛化能力。 显示英文摘要

摘要： The fine-grained classification of brain tumor subtypes from histopathological whole slide images is highly challenging due to subtle morphological variations and the scarcity of annotated data. Although vision-language models have enabled promising zero-shot classification, their ability to capture fine-grained pathological features remains limited, resulting in suboptimal subtype discrimination. To address these challenges, we propose the Fine-Grained Patch Alignment Network (FG-PAN), a novel zero-shot framework tailored for digital pathology. FG-PAN consists of two key modules: (1) a local feature refinement module that enhances patch-level visual features by modeling spatial relationships among representative patches, and (2) a fine-grained text description generation module that leverages large language models to produce pathology-aware, class-specific semantic prototypes. By aligning refined visual features with LLM-generated fine-grained descriptions, FG-PAN effectively increases class separability in both visual and semantic spaces. Extensive experiments on multiple public pathology datasets, including EBRAINS and TCGA, demonstrate that FG-PAN achieves state-of-the-art performance and robust generalization in zero-shot brain tumor subtype classification.